<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Imitation Learning on WorldSense Tech Blog</title><link>https://worldsensetech.com/en/tags/imitation-learning/</link><description>Recent content in Imitation Learning on WorldSense Tech Blog</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Thu, 13 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://worldsensetech.com/en/tags/imitation-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>The Data Challenge in Robotics: Where Does Robot Learning Data Come From?</title><link>https://worldsensetech.com/en/articles/robot-data-challenge/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0000</pubDate><guid>https://worldsensetech.com/en/articles/robot-data-challenge/</guid><description>&lt;p&gt;The development of large language models has demonstrated that large-scale, diverse data can significantly improve model capabilities. But data scale is only one piece of the puzzle. The Transformer architecture, pre-training objectives, scaling laws, and post-training methods like RLHF all work together to produce today&amp;rsquo;s LLMs.&lt;/p&gt;
&lt;p&gt;But if you&amp;rsquo;ve worked on robotics AI, you know this firsthand: robot data is far harder to come by than language data.&lt;/p&gt;
&lt;p&gt;Why is that? What exactly makes robot data so difficult? And are there solutions?&lt;/p&gt;</description></item></channel></rss>